The Use of the Delphi and Other Consensus Group Methods in Medical Education Research: A Review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
PURPOSE: Consensus group methods, such as the Delphi method and nominal group technique (NGT), are used to synthesize expert opinions when evidence is lacking. Despite their extensive use, these methods are inconsistently applied. Their use in medical education research has not been well studied. The authors set out to describe the use of consensus methods in medical education research and to assess the reporting quality of these methods and results. METHOD: Using scoping review methods, the authors searched the Medline, Embase, PsycInfo, PubMed, Scopus, and ERIC databases for 2009-2016. Full-text articles that focused on medical education and the keywords Delphi, RAND, NGT, or other consensus group methods were included. A standardized extraction form was used to collect article demographic data and features reflecting methodological rigor. RESULTS: Of the articles reviewed, 257 met the inclusion criteria. The Modified Delphi (105/257; 40.8%), Delphi (91/257; 35.4%), and NGT (23/257; 8.9%) methods were most often used. The most common study purpose was curriculum development or reform (68/257; 26.5%), assessment tool development (55/257; 21.4%), and defining competencies (43/257; 16.7%). The reporting quality varied, with 70.0% (180/257) of articles reporting a literature review, 27.2% (70/257) reporting what background information was provided to participants, 66.1% (170/257) describing the number of participants, 40.1% (103/257) reporting if private decisions were collected, 37.7% (97/257) reporting if formal feedback of group ratings was shared, and 43.2% (111/257) defining consensus a priori. CONCLUSIONS: Consensus methods are poorly standardized and inconsistently used in medical education research. Improved criteria for reporting are needed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.077 | 0.190 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it